class_docstr: Visualizes episodes of the policy as it trains.
  
  Usually, this class is used by sending plot=True to Trainer.train().
  
  Args:
      env (garage.Environment): Environment from which to visualize episodes.
          This will be used without copying in the current process but in a
          separate thread, so it should be given a unique copy (in
          particular, do not pass the training environment here, then try to
          pickle it, or you will occasionally get crashes).
      policy (garage.tf.Policy): Policy used to visualize episodes.
      sess (tf.Session): The TensorFlow session to use.
      graph (tf.Graph): The TensorFlow graph to use.
      rollout (callable): The rollout function to call.
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docstr: Return all garage.tf.Plotter's.
  
  Returns:
      list[garage.tf.Plotter]: All the garage.tf.Plotter's
docstr_tok: ["Return","all","garage.tf.Plotter","'s",".","Returns",":","list","[","garage.tf.Plotter","]",":","All","the","garage.tf.Plotter","'s"]
code: def get_plotters():
      """Return all garage.tf.Plotter's.
  
      Returns:
          list[garage.tf.Plotter]: All the garage.tf.Plotter's
  
      """
      return Plotter.__plotters
  
file_path: garage/src/garage/tf/plotter/plotter.py
imports: import atexit
  from collections import namedtuple
  from enum import Enum
  import platform
  from queue import Queue
  from threading import Thread
  import numpy
  import tensorflow
  from garage import rollout
label_desc: Use image node_id 4 for calling the Plotter obj's underlying member method code with example usage: obj.get_plotters() and returns: Plotter